class_docstr: Base class of the tokenizer
docstr: Encode the input sentence(s) into multiple tokens.
  
  Parameters
  ----------
  sentences
      The sentences to tokenize
  output_type
      The type of the output tokens.
      - str means each token is represented by its original text.
      - int means each token is represented by the index in the vocabulary.
  
  Returns
  -------
  tokens
      The output tokens.
docstr_tok: ["Encode","the","input","sentence","(","s",")","into","multiple","tokens",".","Parameters","--","--","--","--","--","sentences","The","sentences","to","tokenize","output_type","The","type","of","the","output","tokens",".","-","str","means","each","token","is","represented","by","its","original","text",".","-","int","means","each","token","is","represented","by","the","index","in","the","vocabulary",".","Returns","--","--","--","-","tokens","The","output","tokens","."]
code: def encode(
      self, sentences: SentencesType, output_type: type = str
  ) -> Union[TokensType, TokenIDsType]:
      """Encode the input sentence(s) into multiple tokens.
  
      Parameters
      ----------
      sentences
          The sentences to tokenize
      output_type
          The type of the output tokens.
          - str means each token is represented by its original text.
          - int means each token is represented by the index in the vocabulary.
  
      Returns
      -------
      tokens
          The output tokens.
      """
      pass
  
imports: import abc
  import itertools
  from ..vocab import Vocab
  from ...utils.registry import Registry
  from typing import List, Tuple, Union, NewType, Optional
label_desc: Use image node_id 1 for calling the BaseTokenizer obj's underlying member method code with example usage: obj.encode(sentences, output_type) without return types